MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) Software Version 4.0

and Evolution. All rights reserved. For permissions, please e-mail: [email protected] The Author 2007. Published by Oxford University Press on behalf of the Society for Molecular Biology

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Revised April 25, 2007

Letters (software)

MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) software version 4.0

Koichiro Tamura1,2, Joel Dudley1, Masatoshi Nei3 and Sudhir Kumar1,4*

1Center for Evolutionary Functional Genomics, The Biodesign Institute, Arizona State

University, Tempe, AZ 85287-5301, USA 2Department of Biological Sciences, Tokyo Metropolitan University, 1-1 Minami-ohsawa,

Hachioji, Tokyo 192-0397, Japan 3Department of Biology and the Institute of Molecular Evolutionary Genetics, The Pennsylvania

State University, University Park, PA 16802, USA 4School of Life Sciences, Arizona State University, Tempe, AZ 85287-4501, USA

*Address for Correspondence:

Sudhir Kumar Biodesign Institute Building A240 Arizona State University

1001 S. McAllister Avenue Tempe, AZ 85287-5301

Tel: 480-727-6949 E-mail: [email protected]

MBE Advance Access published May 7, 2007 by guest on A

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Abstract

We announce the release of the fourth version of MEGA software, which expands

on the existing facilities for editing DNA sequence data from auto-sequencers, mining web-

databases, performing automatic and manual sequence alignment, analyzing sequence

alignments to estimate evolutionary distances, inferring phylogenetic trees, and testing

evolutionary hypotheses. Version 4 includes a unique facility to generate captions, written

in figure legend format, in order to provide natural language descriptions of the models

and methods used in the analyses. This facility aims to promote a better understanding of

the underlying assumptions used in analyses, and of the results generated. Another new

feature is the Maximum Composite Likelihood (MCL) method for estimating evolutionary

distances between all pair of sequences simultaneously, with and without incorporating

rate variation among sites and substitution pattern heterogeneities among lineages. This

MCL method also can be used to estimate transition/transversion bias and nucleotide

substitution pattern without the knowledge of the phylogenetic tree. This new version is a

native 32-bit Windows application with multi-threading and multi-user supports, and it is

also available to run in a Linux desktop environment (via the Wine compatibility layer)

and on Intel-based Macintosh computers under the Parallels program. The current test

version of MEGA is available free of charge at http://www.megasoftware.net.

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Since the early 1990s, MEGA software functionality has evolved to include the creation and

exploration of sequence alignments, the estimation of sequence divergence, the reconstruction

and visualization of phylogenetic trees, and the testing of molecular evolutionary hypotheses.

The three versions of MEGA have been released, and they integrate web-based sequence data

acquisition and alignment capabilities (Figure 1) with the evolutionary analyses (Figure 2),

making it much easier to conduct comparative analyses in a single computing environment

(Kumar, Tamura, and Nei 2004). Over time, MEGA has come to enhance the classroom

learning experience as its use by researchers, educators, and students in diverse disciplines has

expanded (Kumar and Dudley 2007). The fourth version (MEGA4) contains three distinct

newly-developed functionalities, which are outlined below.

First, we have developed a Caption Expert software module that generates descriptions

for every result obtained by MEGA4. This description informs the user of all of the options used

in the analysis, including the data subset actually used (e.g., codon positions included), the

chosen option for the handling of sites with gaps or missing data, the evolutionary model of

substitution (e.g., DNA substitution pattern, uniformity of evolutionary rates among sites, and

homogeneity assumption among lineages), and the methods applied for estimating pairwise

distances and for inferring and testing phylogeny. The caption also includes specific citations for

any method, algorithm, and software used in the given analysis. Two examples of descriptions

generated by the Caption Expert are shown in Figure 3.

The availability of these descriptions is intended to promote a better understanding of the

underlying assumptions used in analyses, and of the results produced. This is needed because

MEGA's intuitive graphical interface makes it easy for both novice and expert users to conduct a

variety of computational and statistical analyses. However, some users may not immediately



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realize the underlying assumptions and data-handling options involved in each analysis. Even

expert molecular and population geneticists may not be able to discern all of the assumptions

implied. In general, we expect a written description of methods and results to be useful for

students and researchers when preparing tables and figures for presentation and publication.

Second, we have now added a Maximum Composite Likelihood (MCL) method for

estimating evolutionary distances (dij) between DNA sequences, which MEGA users frequently

employ for inferring phylogenetic trees, divergence times, and average sequence divergences

between and within groups of sequences. In this approach, the Composite Log Likelihood (CL)

obtained as the sum of log likelihood for all sequence pairs in an alignment is maximized by

fitting the common parameters for nucleotide substitution pattern ( � ) to every sequence pair (i,j):

, ln ( , )i j ijCL l d!=" (Tamura, Nei, and Kumar 2004). This approach was previously referred to

as the “Simultaneous Estimation” (SE) method, because all dij’s are simultaneously estimated

(Tamura, Nei, and Kumar 2004). The MCL approach differs from current approaches for

evolutionary distance estimation, wherein each distance is estimated independently of others

either by analytical formulas or by likelihood methods (independent estimation [IE] approach).

The MCL method has many advantages over the IE approach. To begin with, the IE

method for estimating evolutionary distance for each pair of sequences will often cause rather

large errors unless very long sequences are used. The use of the MCL method reduces these

errors considerably, as a single set of parameters estimated from all sequence pairs is applied to

each distance estimation. When distances are estimated with lower errors, distance-based

methods for inferring phylogenies are expected to be more accurate. This is indeed the case for

the Neighbor-Joining method (Saitou and Nei 1987), as the use of the MCL distances leads to a

much higher accuracy (Tamura, Nei, and Kumar 2004). Even when the topologies estimated are



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the same, the use of the MCL distances often gives higher bootstrap values for the estimated

phylogenetic tree compared to the use of IE distances, as is evident from the example given in

Figure 4 A (MCL: bold, IE: italics).

In addition, the IE distances are not always estimable when pairwise distances are

calculated between very distantly related sequences, because the arguments of logarithms in the

analytical formulas may become negative by chance. The probability of occurrence of such

inapplicable cases increases as the number of sequences in the data increases, the evolutionary

distances become larger, and the substitution pattern becomes more complex (Tamura, Nei, and

Kumar 2004). The use of the MCL method eliminates this problem effectively and allows for

the use of sophisticated models in inferring phylogenies from an increasingly larger number of

diverse sequences.

MEGA4 implements the MCL approach for estimating distances between sequence pairs,

average distances between and within groups, and average pairs overall with their variances

estimated by a bootstrap approach. Our implementation of the MCL method allows for the

consideration of substitution rate variation from site-to-site, using an approximation of the

gamma distribution of evolutionary rates, and the incorporation of heterogeneity of base

composition in different species/sequences. The user also has the flexibility to estimate the

numbers of transition and transversion type substitutions per site separately. Naturally, the MCL

distances can be utilized for inferring phylogenies by the distance-based methods, along with the

bootstrap tests of phylogenies.

MEGA4 implements the MCL approach under the Tamura-Nei (1993) substitution

model, in which the rates of two types of transitional substitutions (between purines [a1] and

between pyrimidines [a2]) and the rate of transversional substitutions (b) are considered



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separately by taking into account the unequal frequencies of four nucleotides (base composition

bias). The MCL estimates of transition/transversion rate ratio have been found to be close to the

true values in previous simulation experiments (Tamura, Nei, and Kumar 2004). We have

utilized this feature to provide users with a facility to compute the relative rates of substitutions

between nucleotides based on the MCL estimates of a1, a2, b, and on the observed frequencies of

the four nucleotides under the Tamura-Nei (1993) model (Figure 3C). For ease of comparison,

we have expressed these substitution rates as relative frequencies of substitutions between

nucleotides such that the sum of all frequencies is 100 (see also Gojobori, Li, and Graur 1982).

Third, we have now programmed MEGA4 to run on some versions of Linux through the

Wine software compatibility layer (www.winehq.org). The first advancement alleviates the

problem of performance degradation (and the need to purchase Windows emulation software)

when using MEGA on Linux. Wine is neither a hardware nor a software emulator, but an open

source tool that allows for the native execution of Windows applications on Linux. Our tests of

MEGA4 running on Linux show the display, stability, and performance to be highly satisfactory

and comparable to the native Windows system (Figure 4 B). Furthermore, investigators now

report MEGA4 running on Intel-based Macintosh computers under the Parallels program as well

as it does on Windows-native personal computers (see Hall 2007). The Parallels program is a

native solution for Macs that permits them to simultaneously run Windows and Macintosh

software.

We have also built support for a multi-user environment, which will allow each user of

the same computer to keep his/her customized settings, including file locations, window sizes,

choice of genetic code table, and previously used analysis options. This feature will facilitate

educational and laboratory usage where a single computer is shared by multiple users.



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In conclusion, MEGA4 now contains a wide array of functionalities for the molecular

evolutionary analysis of data (http://www.megasoftware.net/features.html). It is useful to note

that while we are continuously adding new methods and functions to MEGA, we do not intend to

make it a catalogue of all evolutionary analysis methods available. Rather, it is anticipated to

become a workbench for the exploration of sequence data from evolutionary perspectives.

Acknowledgements

We thank the colleagues, students, and volunteers who spent countless hours testing the

early release versions of MEGA; almost all facets of MEGA’s design and implementation

benefited from these comments. We thank Ms. Linwei Wu for assistance with MEGA website

and handling bugs, and Ms. Kristi Garboushian for editorial support. We thank the two

reviewers for suggesting many useful text additions, which have been included in the Figure 1

legend and in the text. We also thank Drs. Masafumi Nozawa and Barry Hall for comments on

an earlier version of this manuscript. The MEGA software project is supported by research

grants from National Institutes of Health (S. K. and M. N.) and from Japan Society for

Promotion of Sciences (K.T.).



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Figure 1. Sequence alignment editor and web-data mining features in MEGA4. In the

Alignment Explorer (panel A), the integrated web browser (pane B) permits downloading

sequences from online databases directly into the current alignment, without the need for manual

cutting-and-pasting and reformatting. The DNA sequences can be translated to the

corresponding protein sequences by a single mouse click, and the protein sequences can be

aligned by ClustalW (panel E) (Thompson, Higgins, and Gibson 1994) and adjusted manually by

eye. Returning to the nucleotide view automatically aligns the nucleotide sequences according to

the protein alignments, and DNA and protein sequence alignments can be exported in a variety

of formats for use with other programs. Alignment Editor also contains facilities for editing and

importing of trace data files output from DNA sequencers (panel C).

Figure 2. A collection of menus that provide access to many different data analysis options in

MEGA4, including exploration of input data set (A), estimation of evolutionary distances (B),

inferring and testing phylogenetic trees (C), tests of homogeneity of substitution patterns and its

estimation (D), tests of selection (E), alignment of DNA and protein sequences (F), and the

dialog box that provides users with options to select model of substitution and data sub-setting

options (G).

Figure 3. The Tree-Explorer displaying a Neighbor-Joining tree of mitochondrial 16S rRNA

sequences (panel A), and the description generated by the Caption Expert (panel B). Estimates

of the relative probabilities of nucleotide substitutions for 70 control-region sequences of human

mitochondrial DNA sequences are shown in panel C. The gamma shape parameter (a = 0.35)

was estimated using the Yang and Kumar (1996) method, and the rest of the analysis details are

given in panel B. It is worth noting that the Tree Explorer shown in panel A includes a high-



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resolution tree drawing facility that includes displaying trees in a variety of formats, with options

to display/hide branch lengths as well as clade confidence labels, and re-rooting and rearranging

trees, among other functionalities. MEGA4 can export the drawings to graphics programs, and

can export trees in Newick format for use by other programs. Furthermore, MEGA can import

and draw trees from Newick format files that have been estimated by other programs (see panel

2C).

Figure 4. (A) Bootstrap support for the branching order of 16 Laurasiatheria species

reconstructed with MCL approach (bold) and without MCL approach (italics) under the Tamura-

Nei (1993) model (see Figure 3B for rest of the analysis details). The 16S rRNA sequences used

were downloaded from GenBank and were aligned in MEGA4 using CLUSTALW (accession

numbers: AJ428578, NC004029, X72004, AF303109, NC008093, DQ480502, X97336, X79547,

DQ534707, AJ554051, AJ554061, NC000889, NC007704, AB074968, NC005044 and

NC001941). (B) Comparison of MEGA4 performance benchmarks on Windows and Linux

(with Wine application compatibility layer). Identical hardware configuration was used, and

example data sets included in the MEGA4 installation were employed. The results show that

computations executed under Wine are penalized by about 2 seconds, which is attributable to the

need for Wine’s initialization.



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References

Gojobori T.,Li W. H.,Graur D. 1982. Patterns of nucleotide substitution in pseudogenes and

functional genes. J Mol Evol 18:360-369.

Kumar S.,Dudley J. 2007. Bioinformatics for biologists in the genomics era. Bioinformatics (In

press).

Kumar S.,Tamura K.,Nei M. 2004. MEGA3: An integrated software for Molecular Evolutionary

Genetics Analysis and sequence alignment. Brief Bioinform 5:150-163.

Saitou N.,Nei M. 1987. The Neighbor-Joining Method - a New Method for Reconstructing

Phylogenetic Trees. Mol. Biol. Evol. 4:406-425.

Tamura K.,Nei M. 1993. Estimation of the Number of Nucleotide Substitutions in the Control

Region of Mitochondrial-DNA in Humans and Chimpanzees. Mol. Biol. Evol. 10:512-

526.

Tamura K.,Nei M.,Kumar S. 2004. Prospects for inferring very large phylogenies by using the

neighbor-joining method. Proc Natl Acad Sci U S A 101:11030-11035.

Thompson J. D.,Higgins D. G.,Gibson T. J. 1994. Clustal-W - Improving the Sensitivity of

Progressive Multiple Sequence Alignment through Sequence Weighting, Position-

Specific Gap Penalties and Weight Matrix Choice. Nucleic Acids Res. 22:4673-4680.

Yang Z.,Kumar S. 1996. Approximate methods for estimating the pattern of nucleotide

substitution and the variation of substitution rates among sites. Mol Biol Evol 13:650-659.



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